Scores on the Safe Functional Motion Test Are Associated with Prevalent Fractures and Fall History
Bibliographic record
Abstract
PURPOSE: The Safe Functional Motion test (SFM) was developed to measure observed body mechanics and functional motion associated with spine load, balance, strength, and flexibility during everyday tasks to profile modifiable risks for osteoporotic fracture. This cross-sectional study evaluated the associations between SFM score and history of vertebral compression fracture (VCF), hip fracture, and injurious falls, all established predictors of future risk. METHOD: An osteoporosis clinic database was queried for adults with an initial SFM score and corresponding data for prevalent VCF and/or hip fracture, femoral neck bone mineral density (fnBMD), and history of injurious fall (n=847). Multiple logistic regressions, adjusted for age, gender, and fnBMD (and injurious falls in the prevalent fracture analyses), were used to determine whether associations exist between SFM score and prevalent VCF, prevalent hip fracture, and history of injurious fall. RESULTS: SFM score was associated with prevalent VCF (odds ratio [OR]=0.89; 95% CI, 0.79-0.99; p=0.036), prevalent hip fracture (OR=0.77; 95% CI, 0.65-0.92; p=0.004), and history of injurious fall (OR=0.80; 95% CI, 0.70-0.93; p=0.003) after adjusting for other important covariates. CONCLUSIONS: Adults with higher SFM scores ("safer motion" during performance of everyday tasks) were less likely to have a history of fracture or injurious fall. Further study is warranted to evaluate the predictive value of this tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".